TM Machine learning 70

Job description

We are looking for a skilled Machine Learning Engineer to design, develop, and deploy scalable machine learning models that solve real-world business problems. The ideal candidate will work closely with data scientists, software engineers, and product teams to build intelligent, data-driven systems. Key Responsibilities Design, build, and deploy machine learning models and pipelines Analyze large datasets to extract insights and improve model performance Develop and maintain data preprocessing, feature engineering, and model evaluation workflows Optimize models for performance, scalability, and reliability Integrate ML models into production systems and APIs Monitor model performance and retrain models as needed Stay up to date with the latest machine learning techniques and tools Required Skills & Qualifications Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field Strong understanding of machine learning algorithms (supervised, unsupervised, deep learning) Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn) Experience with data processing tools (Pandas, NumPy) Knowledge of SQL and data storage systems Understanding of model evaluation metrics and validation techniques All your information will be kept confidential according to EEO guidelines. Define, implement and manage test automation tools, frameworks and methodologies promoting an automation-first approach across all Quality Assurance activities. Foster and promote a QA Engineering approach, uplifting automation capabilities across the QA team as well as within projects and delivery squads. Define appropriate levels of test automation coverage for new initiatives as well as BAU activities, leading a team of QA Analysts in delivering maintainable and robust automated suites. Promote and foster a ‘shift-left’ approach to QA, demonstrating QA value across design and delivery of solutions. Estimate test automation efforts including resources, licensing and infrastructure required. Work closely with the DevOps practice to embed automated testing in CI/CD pipelines, enabling faster delivery cycles whilst ensuring quality of releases. Actively manage Test Automation tools to ensure frameworks leverage modern QA practices. Mentor and guide a team of QA Analysts in delivering test automation work on time and on budget. Leverage automation tools to generate test data, setup and validate environments. Be a champion for automation and agile ways-of-working, continuously identifying new automation opportunities, managing an automation backlog. Conduct peer reviews of development work. Playing an active role in establishing and maturing the RMIT QA Community of Practice. Assist the QA Manager for Ad Hoc testing duties.